Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I

We propose an approach based on a conditional generative adversarial network (cGAN) for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients. Our training set contains paired pre-implantation and post-implantation CTs of 90 ears. At the training phase, the cGAN learns a mapping from the artifact-affected CTs to the artifact-free CTs. At the inference phase, given new metal-artifact-affected CTs, the cGAN produces CTs in which the artifacts are removed. As a preprocessing step, we also propose a band-wise normalization method, which splits a CT image into three channels according to the intensity value of each voxel and we show that this method improves the performance of the cGAN. We test our cGAN on post-implantation CTs of 74 ears and the quality of the artifact-corrected images is evaluated quantitatively by comparing the segmentations of intra-cochlear anatomical structures, which are obtained with a previously published method, in the real pre-implantation and the artifactcorrected CTs. We show that the proposed method leads to an average surface error of 0.18 mm which is about half of what could be achieved with a previously proposed technique.

[1]  Marcos Ortega,et al.  Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement , 2018, KES.

[2]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[3]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[4]  Paul K Marsden,et al.  PET-MRI: a review of challenges and solutions in the development of integrated multimodality imaging , 2015, Physics in medicine and biology.

[5]  Leslie Ying,et al.  Sparsity-based PET image reconstruction using MRI learned dictionaries , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[6]  U. Rajendra Acharya,et al.  Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network , 2017, J. Comput. Sci..

[7]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[8]  Simon J. Doran,et al.  Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Yannan Jin,et al.  Reducing Metal Streak Artifacts in CT Images via Deep Learning : Pilot Results , 2017 .

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[13]  Andrew J Reader,et al.  Patch-based image reconstruction for PET using prior-image derived dictionaries , 2016, Physics in medicine and biology.

[14]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Tapani Raiko,et al.  Semi-supervised Learning with Ladder Networks , 2015, NIPS.

[16]  Guobao Wang,et al.  PET Image Reconstruction Using Kernel Method , 2015, IEEE Transactions on Medical Imaging.

[17]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[18]  Simon R. Arridge,et al.  PET Image Reconstruction Using Information Theoretic Anatomical Priors , 2011, IEEE Transactions on Medical Imaging.

[19]  L. Shepp,et al.  Maximum Likelihood Reconstruction for Emission Tomography , 1983, IEEE Transactions on Medical Imaging.

[20]  Jin Keun Seo,et al.  Machine-learning-based nonlinear decomposition of CT images for metal artifact reduction , 2017, 1708.00244.

[21]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[22]  Boudewijn P. F. Lelieveldt,et al.  Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks , 2017, MICCAI.

[23]  Hossein Rabbani,et al.  Diabetic Retinopathy Grading by Digital Curvelet Transform , 2012, Comput. Math. Methods Medicine.

[24]  Debjani Chakraborty,et al.  Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. , 2017, Biomedical optics express.

[25]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[26]  Syed Muhammad Anwar,et al.  Deep Learning in Medical Image Analysis , 2017 .

[27]  Ana Maria Mendonça,et al.  End-to-End Adversarial Retinal Image Synthesis , 2018, IEEE Transactions on Medical Imaging.

[28]  Omid Majdani,et al.  Automatic Segmentation of Intracochlear Anatomy in Conventional CT , 2011, IEEE Transactions on Biomedical Engineering.

[29]  Ge Wang,et al.  Metal Artifact Reduction in CT: Where Are We After Four Decades? , 2016, IEEE Access.

[30]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[31]  Hengyong Yu,et al.  Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography , 2017, IEEE Transactions on Medical Imaging.

[32]  Marc Niethammer,et al.  Quicksilver: Fast predictive image registration – A deep learning approach , 2017, NeuroImage.

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.